Lidar system design to mitigate lidar cross-talk

ABSTRACT

Aspects of the present disclosure involve systems, methods, and devices for mitigating Lidar cross-talk. Consistent with some embodiments, a Lidar system is configured to include one or more noise source detectors that detect noise signals that may produce noise in return signals received at the Lidar system. A noise source detector comprises a light sensor to receive a noise signal produced by a noise source and a timing circuit to provide a timing signal indicative of a direction of the noise source relative to an autonomous vehicle on which the Lidar system is mounted. A noise source may be an external Lidar system or a surface in the surrounding environment that is reflecting light signals such as those emitted by an external Lidar system.

CLAIM FOR PRIORITY

This application claims the benefit of priority of U.S. ProvisionalApplication No. 62/714,042, filed Aug. 2, 2018 and 62/714,043, filedAug. 2, 2018, the benefit of priority of each of which is hereby claimedherein, and which applications are hereby incorporated herein byreference in their entireties.

TECHNICAL FIELD

The subject matter disclosed herein relates to light detection andranging (Lidar) systems. In particular, example embodiments may relateto a Lidar system design to mitigate Lidar cross-talk.

BACKGROUND

Lidar is a radar-like system that uses lasers to createthree-dimensional representations of surrounding environments. A Lidarunit includes at least one emitter paired with a receiver to form achannel, though an array of channels may be used to expand the field ofview of the Lidar unit. During operation, each channel emits a lightsignal into the environment that is reflected off of the surroundingenvironment back to the receiver. A single channel provides a singlepoint of ranging information. Collectively, channels are combined tocreate a point cloud that corresponds to a three-dimensionalrepresentation of the surrounding environment. The Lidar unit alsoincludes circuitry to measure the time of flight—i.e., the elapsed timefrom emitting the light signal to detecting the return signal. The timeof flight is used to determine the distance of the Lidar unit to thedetected object.

Increasingly, Lidar is finding applications in autonomous vehicles (AVs)such as partially or fully autonomous cars. An AV that uses Lidar canhave its receiver channel saturated or get significant noise in itspoint cloud when another AV using Lidar is within range. In environmentsin which there are a large number of AVs using Lidar, this type ofcrosstalk is extremely problematic because it is likely to cause issueswith down-stream processes that use the Lidar data for vehicleperception, prediction, and motion planning.

BRIEF DESCRIPTION OF THE DRAWINGS

Various ones of the appended drawings merely illustrate exampleembodiments of the present inventive subject matter and cannot beconsidered as limiting its scope.

FIG. 1 is a block diagram illustrating an example autonomous vehicle(AV) system, according to some embodiments.

FIG. 2 is block diagram illustrating a Lidar system, which may beincluded as part of the AV system illustrated in FIG. 1, according tosome embodiments.

FIG. 3A is a diagram illustrating a Lidar system that includes a noisesource detector, according to some embodiments.

FIG. 3B is a diagram illustrating a Lidar system that includes multiplenoise source detectors, according to some embodiments.

FIGS. 4-7 are flowcharts illustrating example operations of the AVsystem in performing a method for detecting and tracking a noise source,according to some embodiments.

FIG. 8 is a diagrammatic representation of a machine in the example formof a computer system within which a set of instructions for causing themachine to perform any one or more of the methodologies discussed hereinmay be executed.

DETAILED DESCRIPTION

Reference will now be made in detail to specific example embodiments forcarrying out the inventive subject matter. Examples of these specificembodiments are illustrated in the accompanying drawings, and specificdetails are set forth in the following description in order to provide athorough understanding of the subject matter. It will be understood thatthese examples are not intended to limit the scope of the claims to theillustrated embodiments. On the contrary, they are intended to coversuch alternatives, modifications, and equivalents as may be includedwithin the scope of the disclosure.

Aspects of the present disclosure address the forgoing issues with Lidarcrosstalk in autonomous systems and others with systems, methods, anddevices to detect, track, and mitigate effects of Lidar crosstalk causedby one or more noise sources.

In some embodiments, a Lidar system is configured to include one or morenoise source detectors that detect noise signals that may produce noisein return signals received at the Lidar system. A noise source detectorcomprises a light sensor to receive a noise signal produced by a noisesource and a timing circuit to provide a timing signal (e.g., atimestamp) indicative of a direction of the noise source relative to anautonomous vehicle on which the Lidar system is mounted. A noise sourcemay be an external Lidar system (e.g., a Lidar system of anothervehicle) or a surface in the surrounding environment that is reflectinglight signals (e.g., emitted by the external Lidar system).

The light sensor is tuned to receive light signals that are the samewavelength as the light signals received by the detectors in eachchannel of the Lidar system in which the noise source detector isincluded. In some embodiments, the light sensor may also be tuned toreceive light signals of other wavelengths, such as those that may beutilized by Lidar systems of other manufacturers. The light sensor mayhave a wider vertical field of view than a horizontal field of view soas to reduce false positive noise source detections. The light sensoralso measures an intensity of received noise signals, which may be usedby downstream processes to classify the noise source as an externalLidar system or negligible noise source.

The timing circuit maintains a clock signal and uses the clock signal togenerate a timestamp corresponding to a time at which a noise signal isreceived by a corresponding light sensor. The timestamp indicates aposition of the noise signal received within a spin cycle of the Lidarsystem and can be correlated to the direction of the noise sourcerelative to the autonomous vehicle. For example, the Lidar system mayinclude an array of channels that continuously rotate around a centralaxis of the Lidar system along with the light sensor during operation ofthe autonomous vehicle. The “spin cycle” of the Lidar system refers to acomplete rotation of these elements around the central axis of the Lidarsystem. Given that the array of channels and light sensor rotate aroundthe central axis at a fixed rate (also referred to as “spin rate”), aduration of each spin cycle is fixed. Thus, the time at which a noisesignal is received by the light sensor may be correlated to a positionof the light sensor within the spin cycle based on the duration of thespin cycle.

The Lidar system also includes circuitry to measure the time of flight(ToF), which is used to determine the distance of the Lidar unit to thedetected object. This type of circuit generally requires a high level ofprecision to ensure distances of detected objects can be accuratelycomputed. On the other hand, the timing circuitry of a noise signaldetector does not require such precision, and thus, the timing circuitryof the noise signal detector can be much less complex and occupy lessspace than the circuitry used to measure ToF. In other words, the timingcircuitry of the noise signal detector operates at a lower level ofprecision than the circuitry used to measure ToF.

As the array of channels rotate around the central axis, each channelemits light signals into the surrounding environment and receives returnsignals corresponding to reflections of the emitted lights signals offof the surrounding environment. The direction at which the array ofchannels emits the light signals may be referred to as a “scanningdirection” of the Lidar system. In embodiments in which the Lidar systemincludes a single noise source detector, the noise source detector mayalso rotate around the central axis of the Lidar system and may bepositioned at or about 180 degrees from a center of the scanningdirection of the Lidar system.

In embodiments in which a Lidar system includes multiple noise sourcedetectors, the noise source detectors may be evenly spaced around thecentral axis and may also be rotated around the central axis. In theseembodiments, each noise source detector operates in the same manner asdescribed above, but expanding the number of noise source detectorsenables the Lidar system to detect direct illumination by an externalLidar system even if both Lidar systems are scanning in a synchronouspattern.

In some embodiments, an autonomous vehicle system for controlling avehicle comprises a Lidar unit to provide ranging information for thevehicle, a noise source detector to detect a noise signal producingnoise in one or more return signals being received at the Lidar unit,and a vehicle computing system. The noise source detector detects anoise signal produced by a noise source, and generates a timestampcomprising a time at which the noise signal is received. The noisesource detector communicates noise data to the vehicle computing system.The noise data comprises a measured intensity of the noise signalcorresponding to the noise source and a time signal (e.g., timestamp)indicative of the direction of the noise source relative to the AVsystem.

The vehicle computing system is configured to detect a noise source byprocessing the noise data provided by the noise source detector. Thedetecting of the noise source includes determining a direction of thenoise source relative to the vehicle. The vehicle computing systemdetermines the direction of the noise source based on the timestampgenerated by the noise source detector that corresponds to the time atwhich the noise signal is received. In particular, the vehicle computingsystem determines the direction of the noise source relative to the AVsystem by correlating the timestamp to a position of the noise sourcedetector in a spin cycle of the Lidar unit based on a spin rate of theLidar unit (e.g., a rate at which the array of channels completes thespin cycle) and correlating the position of the noise source detector inthe spin cycle to the direction of the noise source relative to the AVsystem based on a position of the vehicle relative to the surroundingenvironment and a position of the noise source detector relative to thearray of channels.

The detecting of the noise source performed by the vehicle computingsystem also includes determining a classification of the noise sourcebased on the intensity of the noise signal. The noise source may beclassified as either an external Lidar system (e.g., a Lidar system ofanother vehicle or a surface in the surrounding environment that isreflecting light signals emitted by the external Lidar system) or anegligible noise source. The vehicle computing system may determine theclassification of the noise source by comparing the intensity of thenoise signal to a threshold. For example, the vehicle computing systemmay classify the noise source as an external Lidar system based on theintensity of the noise signal exceeding the threshold. Otherwise, thevehicle computing system may classify noise source as a negligible noisesource.

The vehicle computing system is further configured to track the noisesource as it moves within the surrounding environment. The vehiclecomputing system may track the source of noise by estimating an initiallocation of the noise source based on a direction of the noise sourcedetermined based on a first noise signal and determining a predictedlocation of the noise source based on the initial location. Uponreceiving noise data corresponding to a second noise signal received atthe noise source detector, the vehicle computing system associates thesecond noise signal with the noise source based on the predictedlocation of the noise source and updates the predicted location of thenoise source based on the second noise signal. The vehicle computersystem may continue to associate subsequently received noise signalswith the noise source based on predicted locations, and may continue toupdate predicted locations of the noise source based on the subsequentlyreceived noise signals.

The vehicle computing system also generates state data to describe thenoise source and controls one or more operations of the vehicle based onthe state data. The state data may comprise the classification of thenoise source, a direction of the noise source relative to the vehicle,current locations of the noise source, and predicted locations of thenoise source.

Upon detecting a noise source, a perception system of the vehiclecomputing system may take preventative action to mitigate the effects ofthe noise. For example, as part of a sensor fusion process wherebyinformation from multiple sensors of the autonomous vehicle system isfused together, the perception system may emphasize information receivedfrom certain sensors, mask out information from other sensors that maybe less reliable due to the noise caused by the noise source, and/orchange the type of filtering used in the sensor fusion.

With reference to FIG. 1, an example autonomous vehicle (AV) system 100is illustrated, according to some embodiments. To avoid obscuring theinventive subject matter with unnecessary detail, various functionalcomponents that are not germane to conveying an understanding of theinventive subject matter have been omitted from FIG. 1. However, askilled artisan will readily recognize that various additionalfunctional components may be included as part of the AV system 100 tofacilitate additional functionality that is not specifically describedherein.

The AV system 100 is responsible for controlling a vehicle. The AVsystem 100 is capable of sensing its environment and navigating withouthuman input. The AV system 100 can include a ground-based autonomousvehicle (e.g., car, truck, bus, etc.), an air-based autonomous vehicle(e.g., airplane, drone, helicopter, or other aircraft), or other typesof vehicles (e.g., watercraft).

The AV system 100 includes a vehicle computing system 102, one or moresensors 104, and one or more vehicle controls 116. The vehicle computingsystem 102 can assist in controlling the AV system 100. In particular,the vehicle computing system 102 can receive sensor data from the one ormore sensors 104, attempt to comprehend the surrounding environment byperforming various processing techniques on data collected by thesensors 104, and generate an appropriate motion path through suchsurrounding environment. The vehicle computing system 102 can controlthe one or more vehicle controls 116 to operate the AV system 100according to the motion path.

As illustrated in FIG. 1, the vehicle computing system 102 can includeone or more computing devices that assist in controlling the AV system100. Vehicle computing system 102 can include a localizer system 106, aperception system 108, a prediction system 110, a motion planning system112, and a noise processing system 120 that cooperate to perceive thedynamic surrounding environment of the AV system 100 and determine atrajectory describing a proposed motion path for the AV system 100.Vehicle computing system 102 can additionally include a vehiclecontroller 114 configured to control the one or more vehicle controls116 (e.g., actuators that control gas flow (propulsion), steering,braking, etc.) to execute the motion of the AV system 100 to follow thetrajectory.

In particular, in some implementations, any one of the localizer system106, the perception system 108, the prediction system 110, the motionplanning system 112, or the noise processing system 120 can receivesensor data from the one or more sensors 104 that are coupled to orotherwise included within the AV system 100. As examples, the one ormore sensors 104 can include a Lidar system 118, a Radio Detection andRanging (RADAR) system, one or more cameras (e.g., visible spectrumcameras, infrared cameras, etc.), and/or other sensors. The sensor datacan include information that describes the location of objects withinthe surrounding environment of the AV system 100.

As one example, for Lidar system 118, the sensor data can include pointdata that includes the location (e.g., in three-dimensional spacerelative to the Lidar system 118) of a number of points that correspondto objects that have reflected an emitted light. For example, Lidarsystem 118 can measure distances by measuring the ToF that it takes ashort light pulse to travel from the sensor(s) 104 to an object andback, calculating the distance from the known speed of light. The pointdata further includes an intensity value for each point that can provideinformation about the reflectiveness of the objects that have reflectedan emitted light.

Additionally, the sensor data for the Lidar system 118 also includesnoise data generated by one or more noise source detectors of the Lidarsystem 118. A noise source detector includes a sensor and circuitry todetect noises sources that may produce noise in the point data output bythe Lidar system 118. A noise source may, for example, be a Lidar systemof another AV or a surface reflecting signals emitted by an externalLidar system of another AV. Noise data generated by a noise sourcedetector may include an indication of a direction of a noise sourcerelative to the AV system 100 along with an intensity of one or morenoise signals produced by the noise source. The indication may comprisea timestamp corresponding to a time at which a noise signal produced bythe noise source is received at the noise source detector. As will bediscussed in further detail below, the timestamp may be correlated withthe direction of the noise source relative to the AV system 100.

As another example, for RADAR systems, the sensor data can include thelocation (e.g., in three-dimensional space relative to the RADAR system)of a number of points that correspond to objects that have reflected aranging radio wave. For example, radio waves (e.g., pulsed orcontinuous) transmitted by the RADAR system can reflect off an objectand return to a receiver of the RADAR system, giving information aboutthe object's location and speed. Thus, a RADAR system can provide usefulinformation about the current speed of an object.

As yet another example, for cameras, various processing techniques(e.g., range imaging techniques such as, for example, structure frommotion, structured light, stereo triangulation, and/or other techniques)can be performed to identify the location (e.g., in three-dimensionalspace relative to a camera) of a number of points that correspond toobjects that are depicted in imagery captured by the camera. Othersensor systems can identify the location of points that correspond toobjects as well.

As another example, the one or more sensors 104 can include apositioning system 122. The positioning system 122 can determine acurrent position of the AV system 100. The positioning system 122 can beany device or circuitry for analyzing the position of the AV system 100.For example, the positioning system 122 can determine position by usingone or more of inertial sensors; a satellite positioning system, basedon Internet Protocol (IP) address, by using triangulation and/orproximity to network access points or other network components (e.g.,cellular towers, WiFi access points, etc.); and/or other suitabletechniques. The position of the AV system 100 can be used by varioussystems of the vehicle computing system 102.

Thus, the one or more sensors 104 can be used to collect sensor datathat includes information that describes the location (e.g., inthree-dimensional space relative to the AV system 100) of points thatcorrespond to objects within the surrounding environment of the AVsystem 100.

In addition to the sensor data, the perception system 108, predictionsystem 110, motion planning system 112, and/or the noise processingsystem 120 can retrieve or otherwise obtain map data 124 that providesdetailed information about the surrounding environment of the AV system100. The map data 124 can provide information regarding: the identityand location of different travelways (e.g., roadways, alleyways, trails,and other paths designated for travel), road segments, buildings, orother items or objects (e.g., lampposts, crosswalks, curbing, etc.);known reflectiveness (e.g., radiance) of different travelways (e.g.,roadways), road segments, buildings, or other items or objects (e.g.,lampposts, crosswalks, curbing, etc.); the location and directions oftraffic lanes (e.g., the location and direction of a parking lane, aturning lane, a bicycle lane, or other lanes within a particular roadwayor other travelway); traffic control data (e.g., the location andinstructions of signage, traffic lights, or other traffic controldevices); and/or any other map data that provides information thatassists the vehicle computing system 102 in comprehending and perceivingits surrounding environment and its relationship thereto.

In addition, according to an aspect of the present disclosure, the mapdata 124 can include information that describes a significant number ofnominal pathways through the world. As an example, in some instances,nominal pathways can generally correspond to common patterns of vehicletravel along one or more lanes (e.g., lanes on a roadway or othertravelway). For example, a nominal pathway through a lane can generallycorrespond to a center line of such lane.

The noise processing system 120 receives some or all of the sensor datafrom sensors 104 and processes the sensor data to detect and tracksources of noise. More specifically, the noise processing system 120receives noise data from the Lidar system 118 and processes the noisedata to detect and track noise sources. Accordingly, the noiseprocessing system 120 may use the noise data to determine a direction ofa noise source relative to the AV system 100. In particular, the Lidarsystem 118 may determine a direction of the noise source based on acorrelation between a timestamp in the noise data and a position of acorresponding noise source detector as it rotates around a central axisof the Lidar system 118.

The noise processing system 120 also uses the noise data to classifynoise sources (e.g., as either an external Lidar system or a negligiblenoise source). The noise processing system 120 may classify a noisesource based on an intensity of a noise signal received at a noisesource detector. More specifically, the noise processing system 120 mayclassify the noise source by comparing the intensity of the noise signalproduced by the noise source with a threshold intensity.

The noise processing system 120 may track a noise source as it movesthroughout a surrounding environment continuing to produce noise signalsthat are received by a noise source detector of the Lidar system 118.For example, the noise processing system 120 may associate asubsequently received noise signal with a detected noise source bycorrelating a source direction of the subsequently received noisesignals with a predicted location of the detected noise source. Thepredicted location of the detected noise source may be determined basedon an initial direction of the noise source determined based on aninitial noise signal produced by the noise source.

The localizer system 106 receives the map data 124 and some or all ofthe sensor data from sensors 104 and generates vehicle poses for the AVsystem 100. A vehicle pose describes the position and attitude of thevehicle. The position of the AV system 100 is a point in athree-dimensional space. In some examples, the position is described byvalues for a set of Cartesian coordinates, although any other suitablecoordinate system may be used. The attitude of the AV system 100generally describes the way in which the AV system 100 is oriented atits position. In some examples, attitude is described by a yaw about thevertical axis, a pitch about a first horizontal axis, and a roll about asecond horizontal axis. In some examples, the localizer system 106generates vehicle poses periodically (e.g., every second, every halfsecond, etc.). The localizer system 106 appends time stamps to vehicleposes, where the time stamp for a pose indicates the point in time thatis described by the pose. The localizer system 106 generates vehicleposes by comparing sensor data (e.g., remote sensor data) to map data124 describing the surrounding environment of the AV system 100.

In some examples, the localizer system 106 includes one or morelocalizers and a pose filter. Localizers generate pose estimates bycomparing remote sensor data (e.g., Lidar, RADAR, etc.) to map data 124.The pose filter receives pose estimates from the one or more localizersas well as other sensor data such as, for example, motion sensor datafrom an IMU, encoder, odometer, and the like. In some examples, the posefilter executes a Kalman filter or other machine learning algorithm tocombine pose estimates from the one or more localizers with motionsensor data to generate vehicle poses.

The perception system 108 can identify one or more objects that areproximate to the AV system 100 based on sensor data received from theone or more sensors 104 and/or the map data 124. In particular, in someimplementations, the perception system 108 can determine, for eachobject, state data that describes a current state of such object. Asexamples, the state data for each object can describe an estimate of theobject's: current location (also referred to as position); current speed(also referred to as velocity); current acceleration; current heading;current orientation; size/footprint (e.g., as represented by a boundingshape such as a bounding polygon or polyhedron); class (e.g., vehicleversus pedestrian versus bicycle versus other); yaw rate; specular ordiffuse reflectivity characteristics; and/or other state information.

In some implementations, the perception system 108 can determine statedata for each object over a number of iterations. In particular, theperception system 108 can update the state data for each object at eachiteration. Thus, the perception system 108 can detect and track objects(e.g., vehicles) that are proximate to the AV system 100 over time.

The prediction system 110 can receive the state data from the perceptionsystem 108 and predict one or more future locations for each objectbased on such state data. For example, the prediction system 110 canpredict where each object will be located within the next 5 seconds, 10seconds, 20 seconds, and so forth. As one example, an object can bepredicted to adhere to its current trajectory according to its currentspeed. As another example, other, more sophisticated predictiontechniques or modeling can be used.

The motion planning system 112 can determine a motion plan for the AVsystem 100 based at least in part on the predicted one or more futurelocations for the object provided by the prediction system 110 and/orthe state data for the object provided by the perception system 108.Stated differently, given information about the current locations ofobjects and/or predicted future locations of proximate objects, themotion planning system 112 can determine a motion plan for the AV system100 that best navigates the AV system 100 relative to the objects atsuch locations.

The motion plan can be provided from the motion planning system 112 to avehicle controller 114. In some implementations, the vehicle controller114 can be a linear controller that may not have the same level ofinformation about the environment and obstacles around the desired pathof movement as is available in other computing system components (e.g.,the perception system 108, prediction system 110, motion planning system112, etc.). Nonetheless, the vehicle controller 114 can function to keepthe AV system 100 reasonably close to the motion plan.

More particularly, the vehicle controller 114 can be configured tocontrol motion of the AV system 100 to follow the motion plan. Thevehicle controller 114 can control one or more of propulsion and brakingof the AV system 100 to follow the motion plan. The vehicle controller114 can also control steering of the AV system 100 to follow the motionplan. In some implementations, the vehicle controller 114 can beconfigured to generate one or more vehicle actuator commands and tofurther control one or more vehicle actuators provided within vehiclecontrols 116 in accordance with the vehicle actuator command(s). Vehicleactuators within vehicle controls 116 can include, for example, asteering actuator, a braking actuator, and/or a propulsion actuator.

Each of the localizer system 106, the perception system 108, theprediction system 110, the motion planning system 112, the noiseprocessing system 120, and the vehicle controller 114 can includecomputer logic utilized to provide desired functionality. In someimplementations, each of the localizer system 106, the perception system108, the prediction system 110, the motion planning system 112, thenoise processing system 120, and the vehicle controller 114 can beimplemented in hardware, firmware, and/or software controlling ageneral-purpose processor. For example, in some implementations, each ofthe localizer system 106, the perception system 108, the predictionsystem 110, the motion planning system 112, the noise processing system120 and the vehicle controller 114 includes program files stored on astorage device, loaded into a memory and executed by one or moreprocessors. In other implementations, each of the localizer system 106,the perception system 108, the prediction system 110, the motionplanning system 112, the noise processing system 120, and the vehiclecontroller 114 includes one or more sets of computer-executableinstructions that are stored in a tangible computer-readable storagemedium such as RAM, hard disk, or optical or magnetic media.

FIG. 2 is block diagram illustrating the Lidar system 118, which may beincluded as part of the AV system 100, according to some embodiments. Toavoid obscuring the inventive subject matter with unnecessary detail,various functional components that are not germane to conveying anunderstanding of the inventive subject matter have been omitted fromFIG. 2. However, a skilled artisan will readily recognize that variousadditional functional components may be included as part of the Lidarsystem 118 to facilitate additional functionality that is notspecifically described herein.

As shown, the Lidar system 118 comprises channels 200-0 to 200-N. Thechannels 200-0 to 200-N collectively form an array of channels 201.Individually, each of the channels 200-0 to 200-N outputs point datathat provides a single point of ranging information. Collectively, thepoint data output by each of the channels 200-0 to 200-N (i.e., pointdata_(1-N)) is combined to create a point cloud that corresponds to athree-dimensional representation of the surrounding environment.

Each the channels 200-0 to 200-N comprises an emitter 202 paired with adetector 204. The emitter 202 emits a light signal (e.g., a lasersignal) into the environment that is reflected off the surroundingenvironment and returned back to a sensor 206 (e.g., an opticaldetector) in the detector 204. The signal that is reflected back to thesensor 206 is referred to as a “return signal.” The sensor 206 providesthe return signal to a read-out circuit 208 and the read-out circuit208, in turn, outputs the point data based on the return signal. Thepoint data comprises a distance of the Lidar system 118 from a detectedsurface (e.g., a road) that is determined by the read-out circuit 208 bymeasuring the ToF, which is the elapsed time between the emitter 202emitting the light signal and the detector 204 detecting the returnsignal. To this end, the read-out circuit 208 includes timing circuitryto precisely and accurately measure the ToF.

During operation of the Lidar system 118, the array of channels 201rotates around a central axis of the Lidar system 118. As the array ofchannels 201 rotates around the central axis, each of the channels 200-0to 200-N emits light signals into the surrounding environment andreceives return signals. The direction at which the array of channels201 emits the light signals may be referred to as a “scanning direction”of the Lidar system 118.

As shown, the Lidar system 118 also comprises noise source detectors210-1 to 210-M. Each of the noise source detectors 210-1 to 210-M arecapable of detecting a noise source that may be producing noise in thepoint data output by the array of channels 201. Each of the noise sourcedetectors 210-1 to 210-M comprises a light sensor 212 and a timingcircuit 214. As with the array of channels 201, the noise sourcedetectors 210-1 to 210-M rotate around a central axis of the Lidarsystem 118. A complete rotation of the array of channels 201 and thenoise source detectors 210-1 to 210-M around the central axis of theLidar system 118 may be referred to as a “spin cycle.” The array ofchannels 201 and the noise source detectors 210-1 to 210-M may rotatearound the central axis at a fixed rate, which is referred to as a “spinrate.”

A light sensor 212 comprises a light sensor (e.g., an optical detector)that is tuned to receive light signals that are the same wavelengths asthe light signals received by the sensors 206 of each of the channels200-0 to 200-N. For example, the light sensor 212 may be configured toutilize the same frequency band filtering techniques employed in thesensor 206 of each of the channels 200-0 to 200-N. In some embodiments,the light sensor 212 may also be tuned to receive light signals of otherwavelengths, such as those that may be utilized by Lidar systems ofother manufacturers. The light sensor 212 may be configured to have awider vertical field of view than a horizontal field of view so as toreduce false positive noise source detections such as those that mightbe caused by reflections of light signals emitted by an emitter 202 ofthe array of channels 201. The light sensor 212 also measures anintensity (e.g., an amplitude) of received noise signals, which may beused by the noise processing system 120 to classify the noise source aseither an external Lidar system or a negligible noise source.

The timing circuit 214 maintains a clock signal and uses the clocksignal to generate a timestamp corresponding to a time at which a noisesignal is received by a corresponding light sensor 212. The timestampindicates a position of the noise signal received within a spin cycle ofthe Lidar system 118 and is correlated with a direction of the noisesource relative to the autonomous vehicle. For example, given that thearray of channels 201 and noise source detectors 210-1 to 210-M rotatearound the central axis at a fixed spin rate, a duration of each spincycle is fixed. Thus, the time at which a noise signal is received bythe light sensor 212 may be correlated to a position of the light sensor212 within the Lidar system 118 when it received the noise signal basedon the duration of the spin cycle.

As noted above, the detector 204 of each of the channels 200-0 to 200-Nincludes circuitry to measure a ToF of signals to determine the distanceof the Lidar system 118 to the detected object. This type of circuitgenerally requires a high level of precision to ensure distances ofdetected objects can be accurately computed. On the other hand, thetiming circuit 214 of a noise source detector 210 does not require suchprecision, and thus, the timing circuit 214 of the noise source detectorcan be much less complex and occupy less space than the circuitry usedto measure ToF. In other words, the timing circuit 214 of the noisesource detector 210 operates at a lower level of precision than thecircuitry used to measure ToF.

Each of the noise source detectors 210-1 to 210-M output noise datacomprising timestamps and noise signal intensity measurements. The noisedata output by the noise source detectors 210-1 to 210-M may be combinedwith the point data output by the array of channels 201 to generateoutput data 216. The Lidar system 118 outputs the output data 216 to thevehicle computing system 102 for down-stream processing.

It shall be noted that although FIG. 2 illustrates the Lidar system 118as having multiple instances of the noise source detector 210, in someembodiments, the Lidar system 118 may include only a single instance ofthe noise source detector 210. For example, FIG. 3A illustrates anexample embodiment of the Lidar system 118 in which only a singleinstance of the noise source detector 210 is included. As shown, thenoise source detector 210 and the array of channels 201 rotate around acentral axis 300 of the Lidar system 118. The noise source detector 210is positioned at about 180 degrees from the scanning direction of thearray of channels 201. For example, as illustrated, when receiving anoise signal 302, the noise source detector 210 is about 180 degreesfrom the array of channels 201 as it emits a light signal 304 andreceives a return signal 306. As noted above, upon receiving the noisesignal 302, the noise source detector 210 measures an intensity of thenoise signal 302 and generates a timestamp corresponding to a time atwhich the noise signal 302 is received. The noise source detector 210outputs the intensity and the timestamp to the vehicle computing system102 as noise data. The noise processing system 120 may correlate thetimestamp to a position of the noise source detector 210 in the spincycle, which may be used to determine a direction of the noise sourcerelative to the AV system 100.

FIG. 3B illustrates an example embodiment of the Lidar system 118 inwhich multiple instances of the noise source detector 210 are included.For example, as shown, the Lidar system 118 includes noise sourcedetectors 210-1 to 210-M. The noise source detectors 210-1 to 210-M arepositioned around the central axis 300 at a fixed distance from oneanother. Similar to the embodiment discussed above with respect to FIG.3A, the noise source detectors 210-1 to 210-M and the array of channels201 rotate around the central axis 300 of the Lidar system 118. Each ofthe noise source detectors 210-1 to 210-M is capable of detecting noisesignals as they rotate around the central axis 300. As with theembodiment discussed above, upon receiving the noise signal 302, thereceiving one of the noise source detectors 210-1 to 210-M measures anintensity of the noise signal and generates a timestamp corresponding toa time at which the noise signal is received. By utilizing multipleinstances of the noise source detector 210, the Lidar system 118 candetect direct illumination by an external Lidar system even if both theLidar system 118 and the external Lidar system are scanning in asynchronous pattern.

FIG. 4-7 are flowcharts illustrating example operations of the AV system100 in performing a method 400 for detecting and tracking a noisesource, according to some embodiments. The method 400 may be embodied incomputer-readable instructions for execution by a hardware component(e.g., a processor) such that the operations of the method 400 may beperformed by one or more components of the AV system 100. Accordingly,the method 400 is described below, by way of example with referencethereto. However, it shall be appreciated that the method 400 may bedeployed on various other hardware configurations and is not intended tobe limited to deployment on the vehicle computing system 102.

At operation 405, a noise source detector 210 detects a noise signal.More specifically, a light sensor 212 of the noise source detector 210receives a light signal. The light sensor 212 may receive the lightsignal from a direction other than the scanning direction of the Lidarsystem 118.

At operation 410, the noise source detector 210 generates noise data todescribe the noise signal. The noise data comprises a time signal (e.g.,a timestamp) that is indicative of a direction of a noise sourcecorresponding to the noise signal and a measured intensity of the noisesignal. Further details regarding the generating of the noise data arediscussed below in reference to FIG. 5.

At operation 415, the noise processing system 120 detects a noise sourcecorresponding to the noise signal based on the noise data. As will bediscussed in further detail below, the detecting of the noise sourcecomprises determining the direction of the noise source relative to theAV system 100 and determining a classification of the noise source(e.g., an external Lidar system or a negligible noise source).

At operation 420, the perception system 108 generates state data todescribe the noise source. The state data includes the direction of thenoise source relative to the AV system 100 and the classification of thenoise source. The state data may further include a current location ofthe noise source and/or one or more predicted locations of the noisesource determined by the prediction system 110.

At operation 425, the noise processing system 120 works in conjunctionwith the perception system 108 and prediction system 110 to track thenoise source as it moves through the surrounding environment. Thesesystems may work together to track the noise source based on one or moresubsequent noise signals received by a noise source detector 210 of theLidar system 118. In tracking the noise source, one of several knowntracking techniques may be employed. For example, as will be discussedfurther below, noise processing system 120 may track the noise source byestimating an initial location of the noise source based on thedirection of the noise source (determined as part of operation 415) anddetermining a predicted location of the noise source based on theinitial location. Upon receiving noise data corresponding to asubsequent noise signal received at the noise source detector 210, thenoise processing system 120 associates the subsequent noise signal withthe noise source based on the predicted location of the noise source andthe noise processing system 120 updates the predicted location of thenoise source based on the subsequent noise signal. The noise processingsystem 120 may continue to associate subsequently received noise signalswith the noise source based on predicted locations, and may continue toupdate predicted locations of the noise source based on the subsequentlyreceived noise signals.

At operation 430, the perception system 108 updates the state data thatdescribes the noise source based on the tracking. The updating of thestate data may include updating a current or predicted location of thenoise source.

At operation 435, the vehicle controller 114 controls one or moreoperations of the AV system 100 based on the state data that describesthe noise source. For example, as discussed above, the motion planningsystem 112 determines a motion plan for the AV system 100 based on statedata, and the vehicle controller 114 controls the motion of the AVsystem 100 based on the motion plan.

As shown in FIG. 5, the method 400 may, in some embodiments, includeoperations 411, 412, 413, 416, and 417. Consistent with theseembodiments, the operations 411, 412, and 413 may be performed as partof operation 410 at which the noise source detector 210 generates noisedata.

At operation 411, the timing circuit 214 maintains a clock signal. Theclock signal may be synchronized or otherwise correlated with the spinrate of the Lidar system 118. As an example, the timing circuit 214 mayinitialize the clock signal at onset of operation of the Lidar system118 when the array of channels 201 and the noise source detectors 210-1to 210-M begin spinning around the central axis 300 of the Lidar system118. As another example, the clock signal may comprise a repeating timesignal that corresponds to a duration of a single spin cycle.

At operation 412, the timing circuit 214 uses the clock signal togenerate a timestamp corresponding to a time at which the noise signalis received at the light sensor 212. Given the relationship of the clocksignal and the spin rate of the Lidar system 118, each timestampproduced by the timing circuit 214 corresponds to a position within thespin cycle. Thus, the time at which the noise signal is received at thelight sensor 212 may be correlated with the position of the light sensor212 within a spin cycle of the Lidar system 118 when the light sensor212 received the noise signal based on the relationship of the clocksignal to the spin rate.

At operation 413, the light sensor 212 measures an intensity of thenoise signal. For example, the light sensor 212 may measure an amplitudeof the noise signal.

Consistent with these embodiments, the operations 416 and 417 may beperformed as part of the operation 415, where the noise processingsystem 120 detects the noise source. At operation 416, the noiseprocessing system 120 determines a direction of the noise sourcerelative to the AV system 100 based on the time stamp. The noiseprocessing system 120 may determine the direction of the noise sourcerelative to the AV system 100 based on the position of the light sensor212 within the spin cycle of the Lidar system 118 when the light sensor212 received the noise signal, which may be determined from thetimestamp. Further details regarding the determination of the directionof the noise source relative to the AV system are discussed below inreference to FIG. 6.

At operation 417, the noise processing system 120 determines aclassification of the noise source (e.g., as an external Lidar system ora negligible noise source) based on the intensity of the noise signal.The noise processing system 120 may determine the classification of thenoise source based on a comparison of the intensity of the noise signalto a threshold intensity. Further details regarding the determination ofthe classification of the noise source are discussed below in referenceto FIG. 6.

As shown in FIG. 6, the method 400 may, in some embodiments, includeoperations 605, 610, 615, 620, and 625. Consistent with theseembodiments, the operations 605 and 610 may be performed as part of theoperation 416 where the noise processing system 120 determines thedirection of the noise source relative to the AV system 100.

At operation 605, the noise processing system 120 correlates thetimestamp to a position of the light sensor 212 within the spin cycle ofthe Lidar system 118. More specifically, the noise processing system 120correlates the timestamp to the position of the light sensor 212 withinthe spin cycle of the Lidar system 118 when the light sensor 212received the noise signal. The noise processing system 120 may use theknown spin rate of the Lidar system 118 to calculate a duration of thespin cycle of the Lidar system 118, and use the duration of the spincycle to determine a fraction of a rotation completed by the lightsensor 212 at the time the noise signal was received. The noiseprocessing system 120 may use the fraction of the rotation completed bylight sensor 212 at the time the noise signal was received to determinethe position of the light sensor 212 within the spin cycle based on astarting position of the light sensor 212 within the spin cycle.

For example, assuming a spin rate of 1 Hz (e.g., 1 complete cycle persecond) and a timestamp value of 0.5 seconds, the noise processingsystem 120 may determine the duration of the spin cycle is 1 second andthus, the light sensor 212 had completed half a rotation around thecentral axis of the Lidar system 118 when the noise signal was received.The noise processing system 120 may determine that the light sensor 212was 180 degrees (i.e., a half rotation) from a starting position of thelight sensor 212 in the spin cycle.

At operation 610, the noise processing system 120 correlates theposition of the light sensor 212 within the spin cycle to the directionof the noise source relative to the AV system 100. For example, theLidar system 118 may be mounted on the AV system 100 at a particularorientation, and the noise processing system 120 may utilize the knownmount orientation of the Lidar system 118 to determine the direction ofthe noise source relative to the AV system 100 based on the position ofthe light sensor 212 within the spin cycle of the Lidar system 118.

Consistent with these embodiments, the operations 615, 620, and 625 maybe performed as part of the operation 417 where the noise processingsystem 120 determines a classification of the noise source based on theintensity of the noise signal. At operation 615, the noise processingsystem 120 compares the intensity of the noise signal to a thresholdintensity.

If the noise processing system 120 determines the intensity of the noisesource is greater than the threshold, the noise processing system 120classifies the noise source as an external Lidar system (e.g., anexternal Lidar system of another AV system), at operation 620.Otherwise, the noise processing system 120 classifies the noise sourceas a negligible noise source, at operation 625. For example, the noisesource may be a Lidar system of another AV that is too distant from theAV system 100 to be a cause for concern.

As shown in FIG. 7, the method 400 may, in some embodiments, includeoperations 426, 427, 428, and 429. Consistent with these embodiments,the operations 426, 427, 428, and 429 may be performed as part of theoperation 425 where the noise processing system 120, the perceptionsystem 108, and prediction system 110 work in conjunction to track thesource of noise as it moves through the surrounding environment.

At operation 426, the perception system 108 estimates an initiallocation of the noise source based on the direction of the noise sourcerelative to the AV system 100. In estimating the location of the noisesource, the perception system 108 may utilize at least part of thesensor data from sensors 104. For example, the perception system 108 mayuse point data from the Lidar system 118 to estimate a distance of thenoise source from the AV system 100, which can be combined with thedirection of the noise source relative to the AV system 100 to estimatethe location of the noise source. The perception system 108 may use aKalman filter to combine sensor measurements within the sensor data toimprove accuracy of the initial location estimation.

At operation 427, the prediction system 110 determines a predictedlocation of the noise source based on the estimated initial location ofthe noise source. In determining the predicted location of the noisesource, the prediction system 110 may estimate a current speed of thenoise source and a current trajectory of the noise source. Theprediction system 110 may determine the predicted location of the noisesource using a dynamic model that assumes that the noise source willadhere to the current speed and trajectory. The prediction system 110may estimate the current speed and current trajectory of the noisesource based on one or more of: a current speed of the AV system 100;the direction of the noise source relative to the AV system 100; thedistance of the noise source relative to the AV system 100; aclassification of the noise source; sensor data from any one of thesensors 104; and the map data 124.

At operation 428, the noise processing system 120 associates a secondnoise signal with the noise source based on the predicted location ofthe noise. For example, upon receiving a second noise signal (subsequentto the noise signal received at operation 405, which is referred tobelow as the “first noise signal”), the Lidar system 118 provides noisedata to the noise processing system 120 that includes a time stamp fromwhich the noise processing system 120 may determine a direction of asource of the second noise signal relative to the AV system 100, in thesame manner discussed above with respect to the first noise signal. Aswith the first noise signal, the perception system 108 may use thedirection of the source of the second noise signal to determine alocation of the source of the second noise signal. Based on the locationof the source of the second noise signal being approximately the same asthe predicted location of the noise source corresponding to the firstnoise signal, the noise processing system 120 associates the secondnoise signal with the noise source corresponding to the first noisesignal.

At operation 429, the prediction system 110 updates a predicted locationof the noise source based on the second noise signal. For example, asdiscussed above, the perception system 108 may combine sensormeasurements (e.g., using a Kalman filter) to determine a location ofthe source of the second noise signal, and given that the source of thesecond noise signal is the noise source corresponding to the first noisesignal, the location of the source of the second noise signal is thecurrent location of the noise source. Using the current location of thenoise source, the prediction system 110 may update the predictedlocation of the noise source using the same methodology as discussedabove with respect to operation 427. The prediction system 110 maycontinue to update the predicted location of the noise source assubsequent noise signals are received, and the noise processing system120 may continue to associate the subsequently received noise signalswith the noise source based on the predicted locations of the noisesource determined by the prediction system 110.

FIG. 8 illustrates a diagrammatic representation of a machine 800 in theform of a computer system within which a set of instructions may beexecuted for causing the machine 800 to perform any one or more of themethodologies discussed herein, according to an example embodiment.Specifically, FIG. 8 shows a diagrammatic representation of the machine800 in the example form of a computer system, within which instructions816 (e.g., software, a program, an application, an applet, an app, orother executable code) for causing the machine 800 to perform any one ormore of the methodologies discussed herein may be executed. For example,the instructions 816 may cause the machine 800 to execute the method400. In this way, the instructions 816 transform a general,non-programmed machine into a particular machine 800, such as thevehicle computing system 102, that is specially configured to carry outthe described and illustrated functions in the manner described here. Inalternative embodiments, the machine 800 operates as a standalone deviceor may be coupled (e.g., networked) to other machines. In a networkeddeployment, the machine 800 may operate in the capacity of a servermachine or a client machine in a server-client network environment, oras a peer machine in a peer-to-peer (or distributed) networkenvironment. The machine 800 may comprise, but not be limited to, aserver computer, a client computer, a personal computer (PC), a tabletcomputer, a laptop computer, a netbook, a smart phone, a mobile device,a network router, a network switch, a network bridge, or any machinecapable of executing the instructions 816, sequentially or otherwise,that specify actions to be taken by the machine 800. Further, while onlya single machine 800 is illustrated, the term “machine” shall also betaken to include a collection of machines 800 that individually orjointly execute the instructions 816 to perform any one or more of themethodologies discussed herein.

The machine 800 may include processors 810, memory 830, and input/output(I/O) components 850, which may be configured to communicate with eachother such as via a bus 802. In an example embodiment, the processors810 (e.g., a central processing unit (CPU), a reduced instruction setcomputing (RISC) processor, a complex instruction set computing (CISC)processor, a graphics processing unit (GPU), a digital signal processor(DSP), an application-specific integrated circuit (ASIC), aradio-frequency integrated circuit (RFIC), another processor, or anysuitable combination thereof) may include, for example, a processor 812and a processor 814 that may execute the instructions 816. The term“processor” is intended to include multi-core processors 810 that maycomprise two or more independent processors (sometimes referred to as“cores”) that may execute instructions contemporaneously. Although FIG.8 shows multiple processors 810, the machine 800 may include a singleprocessor with a single core, a single processor with multiple cores(e.g., a multi-core processor), multiple processors with a single core,multiple processors with multiple cores, or any combination thereof.

The memory 830 may include a main memory 832, a static memory 834, and astorage unit 836, both accessible to the processors 810 such as via thebus 802. The main memory 832, the static memory 834, and the storageunit 836 store the instructions 816 embodying any one or more of themethodologies or functions described herein. The instructions 816 mayalso reside, completely or partially, within the main memory 832, withinthe static memory 834, within the storage unit 836, within at least oneof the processors 810 (e.g., within the processor's cache memory), orany suitable combination thereof, during execution thereof by themachine 800.

The I/O components 850 may include components to receive input, provideoutput, produce output, transmit information, exchange information,capture measurements, and so on. The specific I/O components 850 thatare included in a particular machine 800 will depend on the type ofmachine. For example, portable machines such as mobile phones willlikely include a touch input device or other such input mechanisms,while a headless server machine will likely not include such a touchinput device. It will be appreciated that the I/O components 850 mayinclude many other components that are not shown in FIG. 8. The I/Ocomponents 850 are grouped according to functionality merely forsimplifying the following discussion and the grouping is in no waylimiting. In various example embodiments, the I/O components 850 mayinclude output components 852 and input components 854. The outputcomponents 852 may include visual components (e.g., a display such as aplasma display panel (PDP), a light emitting diode (LED) display, aliquid crystal display (LCD), a projector, or a cathode ray tube (CRT)),acoustic components (e.g., speakers), other signal generators, and soforth. The input components 854 may include alphanumeric inputcomponents (e.g., a keyboard, a touch screen configured to receivealphanumeric input, a photo-optical keyboard, or other alphanumericinput components), point-based input components (e.g., a mouse, atouchpad, a trackball, a joystick, a motion sensor, or another pointinginstrument), tactile input components (e.g., a physical button, a touchscreen that provides location and/or force of touches or touch gestures,or other tactile input components), audio input components (e.g., amicrophone), and the like.

Communication may be implemented using a wide variety of technologies.The I/O components 850 may include communication components 864 operableto couple the machine 800 to a network 880 or devices 870 via a coupling882 and a coupling 872, respectively. For example, the communicationcomponents 864 may include a network interface component or anothersuitable device to interface with the network 880. In further examples,the communication components 864 may include wired communicationcomponents, wireless communication components, cellular communicationcomponents, and other communication components to provide communicationvia other modalities. The devices 870 may be another machine or any of awide variety of peripheral devices (e.g., a peripheral device coupledvia a universal serial bus (USB)). EXECUTABLE INSTRUCTIONS AND MACHINESTORAGE MEDIUM

The various memories (e.g., 830, 832, 834, and/or memory of theprocessor(s) 810) and/or the storage unit 836 may store one or more setsof instructions 816 and data structures (e.g., software) embodying orutilized by any one or more of the methodologies or functions describedherein. These instructions, when executed by the processor(s) 810, causevarious operations to implement the disclosed embodiments.

As used herein, the terms “machine-storage medium,” “device-storagemedium,” and “computer-storage medium” mean the same thing and may beused interchangeably in this disclosure. The terms refer to a single ormultiple storage devices and/or media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storeexecutable instructions and/or data. The terms shall accordingly betaken to include, but not be limited to, solid-state memories, andoptical and magnetic media, including memory internal or external toprocessors. Specific examples of machine-storage media, computer-storagemedia, and/or device-storage media include non-volatile memory,including by way of example semiconductor memory devices, e.g., erasableprogrammable read-only memory (EPROM), electrically erasableprogrammable read-only memory (EEPROM), field-programmable gate arrays(FPGAs), and flash memory devices; magnetic disks such as internal harddisks and removable disks; magneto-optical disks; and CD-ROM and DVD-ROMdisks. The terms “machine-storage media,” “computer-storage media,” and“device-storage media” specifically exclude carrier waves, modulateddata signals, and other such media, at least some of which are coveredunder the term “signal medium” discussed below.

Transmission Medium

In various example embodiments, one or more portions of the network 880may be an ad hoc network, an intranet, an extranet, a virtual privatenetwork (VPN), a local-area network (LAN), a wireless LAN (WLAN), awide-area network (WAN), a wireless WAN (WWAN), a metropolitan-areanetwork (MAN), the Internet, a portion of the Internet, a portion of thepublic switched telephone network (PSTN), a plain old telephone service(POTS) network, a cellular telephone network, a wireless network, aWi-Fi® network, another type of network, or a combination of two or moresuch networks. For example, the network 880 or a portion of the network880 may include a wireless or cellular network, and the coupling 882 maybe a Code Division Multiple Access (CDMA) connection, a Global Systemfor Mobile communications (GSM) connection, or another type of cellularor wireless coupling. In this example, the coupling 882 may implementany of a variety of types of data transfer technology, such as SingleCarrier Radio Transmission Technology (1×RTT), Evolution-Data Optimized(EVDO) technology, General Packet Radio Service (GPRS) technology,Enhanced Data rates for GSM Evolution (EDGE) technology, thirdGeneration Partnership Project (3GPP) including 3G, fourth generationwireless (4G) networks, Universal Mobile Telecommunications System(UMTS), High-Speed Packet Access (HSPA), Worldwide Interoperability forMicrowave Access (WiMAX), Long Term Evolution (LTE) standard, othersdefined by various standard-setting organizations, other long-rangeprotocols, or other data transfer technology.

The instructions 816 may be transmitted or received over the network 880using a transmission medium via a network interface device (e.g., anetwork interface component included in the communication components864) and utilizing any one of a number of well-known transfer protocols(e.g., hypertext transfer protocol (HTTP)). Similarly, the instructions816 may be transmitted or received using a transmission medium via thecoupling 872 (e.g., a peer-to-peer coupling) to the devices 870. Theterms “transmission medium” and “signal medium” mean the same thing andmay be used interchangeably in this disclosure. The terms “transmissionmedium” and “signal medium” shall be taken to include any intangiblemedium that is capable of storing, encoding, or carrying theinstructions 816 for execution by the machine 800, and include digitalor analog communications signals or other intangible media to facilitatecommunication of such software. Hence, the terms “transmission medium”and “signal medium” shall be taken to include any form of modulated datasignal, carrier wave, and so forth. The term “modulated data signal”means a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in the signal.

Computer-Readable Medium

The terms “machine-readable medium,” “computer-readable medium,” and“device-readable medium” mean the same thing and may be usedinterchangeably in this disclosure. The terms are defined to includeboth machine-storage media and transmission media. Thus, the termsinclude both storage devices/media and carrier waves/modulated datasignals.

The various operations of example methods described herein may beperformed, at least partially, by one or more processors that aretemporarily configured (e.g., by software) or permanently configured toperform the relevant operations. Similarly, the methods described hereinmay be at least partially processor-implemented. For example, at leastsome of the operations of a method may be performed by one or moreprocessors. The performance of certain of the operations may bedistributed among the one or more processors, not only residing within asingle machine, but deployed across a number of machines. In someexample embodiments, the processor or processors may be located in asingle location (e.g., within a home environment, an office environment,or a server farm), while in other embodiments the processors may bedistributed across a number of locations.

Although the embodiments of the present disclosure have been describedwith reference to specific example embodiments, it will be evident thatvarious modifications and changes may be made to these embodimentswithout departing from the broader scope of the inventive subjectmatter. Accordingly, the specification and drawings are to be regardedin an illustrative rather than a restrictive sense. The accompanyingdrawings that form a part hereof show, by way of illustration, and notof limitation, specific embodiments in which the subject matter may bepracticed. The embodiments illustrated are described in sufficientdetail to enable those skilled in the art to practice the teachingsdisclosed herein. Other embodiments may be used and derived therefrom,such that structural and logical substitutions and changes may be madewithout departing from the scope of this disclosure. This DetailedDescription, therefore, is not to be taken in a limiting sense, and thescope of various embodiments is defined only by the appended claims,along with the full range of equivalents to which such claims areentitled.

Such embodiments of the inventive subject matter may be referred toherein, individually and/or collectively, by the term “invention” merelyfor convenience and without intending to voluntarily limit the scope ofthis application to any single invention or inventive concept if morethan one is in fact disclosed. Thus, although specific embodiments havebeen illustrated and described herein, it should be appreciated that anyarrangement calculated to achieve the same purpose may be substitutedfor the specific embodiments shown. This disclosure is intended to coverany and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, will be apparent, to those of skill inthe art, upon reviewing the above description.

In this document, the terms “a” or “an” are used, as is common in patentdocuments, to include one or more than one, independent of any otherinstances or usages of “at least one” or “one or more.” In thisdocument, the term “or” is used to refer to a nonexclusive or, such that“A or B” includes “A but not B,” “B but not A,” and “A and B,” unlessotherwise indicated. In the appended claims, the terms “including” and“in which” are used as the plain-English equivalents of the respectiveterms “comprising” and “wherein.” Also, in the following claims, theterms “including” and “comprising” are open-ended; that is, a system,device, article, or process that includes elements in addition to thoselisted after such a term in a claim is still deemed to fall within thescope of that claim.

What is claimed is:
 1. A Lidar system comprising: an of array ofchannels to provide ranging information for an autonomous vehicle (AV),a channel of the array of channels comprising a emitter paired with areceiver, the emitter to emit light signals into a surroundingenvironment, the receiver to receive return signals corresponding toreflections of the light signals off the surrounding environment; and anoise source detector coupled to the array of channels, the noise sourcedetector to detect a noise signal producing noise in one or more returnsignals, the noise source detector comprising: a light sensor to receivethe noise signal; and a timing circuit coupled to the light sensor, thetiming circuit to provide an indication of a direction of a noise sourcerelative to the AV, the noise source corresponding to the noise signal.2. The Lidar system of claim 1, wherein: the array of channels and thelight sensor continuously rotate around a central axis of the Lidarsystem, the indication of the direction of the noise source comprises anindication of a position of the light sensor within a spin cycle, thespin cycle corresponding to a complete rotation of the array of channelsand the light sensor around the central axis.
 3. The Lidar system ofclaim 2, wherein the indication of the position of the light sensorwithin the spin cycle comprises a timestamp that correlates to theposition of the light sensor based on a duration of the spin cycle. 4.The Lidar system of claim 3, wherein the providing of the indication ofthe position of the noise source by the timing circuit comprises:generating the timestamp by measuring a time at which the noise signalis received at the light sensor.
 5. The Lidar system of claim 1, whereinthe light sensor is positioned at about 180 degrees from a center of ascanning direction of the array of channels.
 6. The Lidar system ofclaim 1, wherein the light sensor is tuned to a wavelength of the arrayof channels.
 7. The Lidar system of claim 1, wherein the light sensorcomprises a light sensor having a wider vertical field of view relativeto a horizontal field of view.
 8. The Lidar system of claim 1, whereinthe light sensor measures an intensity of the noise signal produced bythe noise source.
 9. The Lidar system of claim 1, wherein the noisesource is one of: an external Lidar system or a surface reflecting alight signal produced by the external Lidar system.
 10. The Lidar systemof claim 1, wherein: the timing circuit is a first timing circuit; thearray of channels includes a second timing circuit to measure a time offlight of return signals; the first timing circuit operates at a firstlevel of precision; and the second timing circuit operates at a secondlevel of precision.
 11. The Lidar system of claim 10, wherein the firstlevel of precision is lower than the second level of precision.
 12. Anoise source detector to detect a noise signal producing noise in one ormore return signals received at a Lidar unit, the noise source detectorcomprising: a light sensor to receive a noise signal; and a timingcircuit coupled to the light sensor, the timing circuit to provide anindication of a direction of a noise source corresponding to the noisesignal relative to an autonomous vehicle (AV) system.
 13. The noisesource detector of claim 12, wherein the indication of the direction ofthe noise source comprises an indication of a position of the lightsensor within a spin cycle of the Lidar unit.
 14. The noise sourcedetector of claim 13, wherein the timing circuit provides the indicationof the position of the light sensor within a spin cycle of the Lidarunit by: maintaining a clock signal; measuring, using the clock signal,a time at which the noise signal is received at the light sensor; andcorrelating the time to the position of the light sensor in the spincycle of the Lidar unit based on a spin rate of the Lidar unit.
 15. Thenoise source detector of claim 13, wherein the light sensor ispositioned at about 180 degrees from a center of a scanning direction ofthe Lidar unit.
 16. The noise source detector of claim 12, wherein thelight sensor is tuned to a wavelength of the Lidar unit.
 17. The noisesource detector of claim 12, wherein: the timing circuit is a firsttiming circuit; the Lidar unit includes a second timing circuit tomeasure a time of flight of return signals; the first timing circuitoperates at a first level of precision; the second timing circuitoperates at a second level of precision; and wherein the first level ofprecision is lower than the second level of precision.
 18. A Lidarsystem comprising: an of array of channels to provide ranginginformation for an autonomous vehicle (AV), a channel of the array ofchannels comprising a emitter paired with a receiver, the emitter toemit light signals into a surrounding environment, the receiver toreceive return signals corresponding to reflections of the light signalsoff the surrounding environment; and a plurality of noise sourcedetectors coupled to the array of channels, the plurality of noisesource detectors to detect noise signals producing noise in one or morereturn signals, each noise source detector comprising: a light sensor toreceive a noise signal; and a timing circuit coupled to the lightsensor, the timing circuit to provide an indication of a direction of anoise source corresponding to the noise signal.
 19. The Lidar system ofclaim 18, wherein: the plurality of noise source detectors arepositioned around a central axis of the Lidar system; and the array ofchannels and the plurality of noise source detectors are rotated aroundthe central axis of the Lidar system.
 20. The Lidar system of claim 18,wherein the providing of the indication of the direction of the noisesource by the timing circuit comprises: generating a timestamp bymeasuring a time at which the noise signal is received at the lightsensor.